ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits

2026-07-25

AI Summary

This work addresses the critical challenge in analog and mixed-signal (AMS) circuit modeling with graph neural networks (GNNs)—the absence of publicly available, high-fidelity parasitic benchmark datasets. To bridge this gap, we introduce ParasGB, the first open-source benchmark for parasitic prediction, constructed from tape-out-validated designs and comprising large-scale heterogeneous RC networks that include node-to-ground capacitances, edge resistances, and coupling capacitances. We identify key challenges such as extreme label imbalance, long-tailed parasitic distributions, and structural heterogeneity. The project provides a unified evaluation protocol, standardized GNN training pipeline, and a fully open platform to enable early-stage parasitic estimation during pre-layout design, thereby establishing a foundation for parasitic-aware design methodologies and reproducible graph learning research in AMS circuits.

Abstract

As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. This makes early-stage estimation of parasitic capacitance and resistance important for parasitic-aware design exploration before full physical implementation. However, progress on GNN-based parasitic modeling has been hindered by the lack of public, high-fidelity RC benchmarks that support reproducible evaluation. To address this gap, we introduce ParasGB, the first open-source benchmark suite for pre-layout parasitic parameter prediction on circuit graphs. ParasGB provides large-scale, heterogeneous RC networks extracted with commercial EDA tools from tape-out-proven designs, together with a unified evaluation protocol covering node-level ground capacitance, edge-level resistance, and edge-level coupling capacitance. Within this framework, we benchmark diverse GNN architectures using a standardized training pipeline and expose challenges such as extreme label imbalance, long-tailed parasitic distributions, and strong structural heterogeneity. By establishing a physically grounded and standardized benchmark for early-stage parasitic prediction, ParasGB provides an open platform for reproducible research on circuit graph learning and parasitic-aware model development. All datasets, preprocessing scripts, and configurations are publicly available in our code repository https://github.com/ShenShan123/ParasGB.git.

Problem

• parasitic estimation
• AMS circuits
• graph benchmark
• RC networks
• pre-layout prediction

Innovation

• Parasitic estimation
• Graph Neural Networks (GNNs)
• AMS circuits
• Benchmark suite
• Pre-layout prediction